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相关概念视频

Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
52.9K

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相关实验视频

Updated: May 24, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
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Published on: April 11, 2025

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使用转移学习来完善盲人和视力低下用户的对象检测模型.

Aradhita Bhandari, Gail S Batutis, Aryan Jain

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    概括

    像YOLOv8这样的物体检测模型可以帮助盲人或视力低下的人 (pBLV). 转移学习有效地调整了这些模型,以识别pBLV导航的关键对象,改善可访问性.

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    相关实验视频

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    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 辅助技术 辅助技术 辅助技术

    背景情况:

    • 智能手机上的物体检测模型,如YOLOv8,有可能帮助盲人或视力低下的人 (pBLV).
    • 现有的模型可能无法识别对pBLV导航至关重要的所有对象.
    • 文本中的共同目标 (COCO) 数据集缺乏与pBLV需求相关的特定类别.

    研究的目的:

    • 将五种转移学习方法进行比较,以适应YOLOv8对象检测模型.
    • 用新的对象类来增强模型,这对于pBLV导航至关重要.
    • 在不同的训练条件下,使用平均平均精度 (mAP-50) 评估性能.

    主要方法:

    • 对YOLOv8s模型应用了五种转移学习技术,使用重新平衡的COCO数据集.
    • 方法包括修改所有预训练的重量,结不同数量的层 (22,21,15),以及几次射击学习.
    • 还进行了超参数调整和应用到更大的YOLOv8xl模型.

    主要成果:

    • 转移学习方法产生了mAP-50分数,范围从0.342 (几次学习) 到0.420 (修改所有权重).
    • 拥有15个冷层的模型实现了最高的mAP-50 (0.419),进一步改进到0.423的超参数调整.
    • 将最佳方法应用于YOLOv8xl模型,结果是mAP-50的0.511.

    结论:

    • 转移学习有效地适应对象检测模型的pBLV用户.
    • 即使使用有限的数据或计算资源,也可以实现显著的性能增长.
    • 这些适应型号可以为视力障碍者增强导航工具.